Identifying causal genetic variants and molecular mechanisms impacting mental health
识别影响心理健康的因果遗传变异和分子机制
基本信息
- 批准号:10571911
- 负责人:
- 金额:$ 61.6万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-04-01 至 2026-01-31
- 项目状态:未结题
- 来源:
- 关键词:ATAC-seqAcuteAffectAllelesAlzheimer&aposs DiseaseBiological AssayBiologyBrainBrain regionCatalogsCellsChromatinCollaborationsCommunitiesComputer softwareComputing MethodologiesDNA SequenceDataDatabasesDetectionDevelopmentDiseaseFutureGene ExpressionGene Expression RegulationGene FrequencyGenesGeneticGenetic DiseasesGenetic LoadGenetic RiskGenetic VariationGenotype-Tissue Expression ProjectHumanIndividualInfluentialsLearningLinkLinkage DisequilibriumMachine LearningMapsMasksMental HealthMental Health AssociationsMental disordersMethodsModelingMolecularMolecular DiseaseMusNational Human Genome Research InstituteNeurodevelopmental DisorderOrganoidsOutcomeParkinson DiseasePathologicPerformancePopulationQuantitative Trait LociReporterReproducibilityResearchResolutionResourcesRiskTissue SampleTissuesTrainingUncertaintyUntranslated RNAVariantWeightWorkbasebiobankbrain cellcausal variantcell typecomputational pipelinesdeep learning modeldisorder riskepigenomeepigenomicsexperimental studyfunctional genomicsgenetic variantgenome resourcegenome wide association studygenomic locusimprovedinsightmachine learning methodmachine learning modelnovelnovel strategiesopen datapolygenic risk scorepreventpublic health relevanceregression algorithmrisk predictionsuccesstherapy developmenttrait
项目摘要
Identifying how genetic variation leads to neurodevelopmental or psychiatric disorders provides new means to
study, predict, prevent and treat disease. Identifying the immediate molecular consequences of disease-
associated genetic variation has necessitated the development of large-scale, multi-tissue functional genomic
resources. Projects such as GTEx, Roadmap Epigenomics Project and PsychENCODE have combined
molecular QTL mapping and epigenomic maps in bulk tissues to interpret various disease-associated genetic
variants. However, few colocalizations between molecular QTLs and traits have been robustly identified and
few causal variants mapped. As tissues like the brain constitute 100s of cell-types, we hypothesize that
existing maps may mask the contributions of disease-associated variation in less-abundant cell types. One
extremely powerful approach to identify cell-type specific molecular effects and their relationship to genetic
diseases is through application of chromatin accessibility data – these data both allow inference of causal cell
types and provide base level resolution gene regulation. Our team has considerable expertise in connecting
GWAS to molecular functions and predicting causal variants through use of chromatin accessibility data. We
have additionally recently collaborated to generate a comprehensive, multi-individual map single cell ATAC-
seq map (scATAC-seq) of six different brain regions to detect causal cell types and predict causal variants.
This work has been recently demonstrated in our fine-mapping study of Alzheimer’s and Parkinson’s disease
(Corces et al, bioRxiv, 2020) but has not been systematically applied to mental health disorders. We propose
to develop statistical genetics and machine learning approaches that advance the use of scATAC-seq data to
connecting mental health GWAS loci to specific cell types, mechanisms and causal variants. In Aim 1, we will
assemble a pipeline that leverages region and cell type-specific scATAC-seq data to identify pathological cell
types for 100s of mental health and brain-related traits. We will also enhance the detection of cell-type specific
molecular mechanisms by extending and applying a novel GWAS/QTL colocalization approach. Throughout
these activities, variants will be validated using massively-parallel reporter assays (MPRA). In Aim 2, we will
develop sophisticated machine learning models that learn regulatory grammars and score variants across the
allele frequency spectrum. Predicted causal variants in GWAS loci will be further assessed with MPRAs in Aim
1 and applied in Aim 3. In Aim 3, we will demonstrate how improved detection of causal variants using our
single-cell informed models aids transferability of polygenic risk scores across populations.
We will provide open resources and reproducible computational methods and pipelines that integrate
single cell chromatin accessibility data from multiple brain regions. This will allow detection cell-type specific
genetic effects and pathological cell types in mental health GWAS, establish robust causal links between
variants, genes and disease, and improve prediction of disease risk.
确定遗传变异如何导致神经发育或精神疾病提供了新的方法
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Anshul Kundaje其他文献
Anshul Kundaje的其他文献
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{{ truncateString('Anshul Kundaje', 18)}}的其他基金
Multi-Omics DACC: The Data Analysis and Coordination Center for the collaborative multi-omics for health and disease initiative
多组学 DACC:健康和疾病协作多组学计划的数据分析和协调中心
- 批准号:
10744561 - 财政年份:2023
- 资助金额:
$ 61.6万 - 项目类别:
A Comprehensive Genomic Community Resource of Transcriptional Regulation
转录调控的综合基因组群落资源
- 批准号:
10411262 - 财政年份:2022
- 资助金额:
$ 61.6万 - 项目类别:
A Comprehensive Genomic Community Resource of Transcriptional Regulation
转录调控的综合基因组群落资源
- 批准号:
10842047 - 财政年份:2022
- 资助金额:
$ 61.6万 - 项目类别:
A Comprehensive Genomic Community Resource of Transcriptional Regulation
转录调控的综合基因组群落资源
- 批准号:
10625529 - 财政年份:2022
- 资助金额:
$ 61.6万 - 项目类别:
Identifying causal genetic variants and molecular mechanisms impacting mental health
识别影响心理健康的因果遗传变异和分子机制
- 批准号:
10380573 - 财政年份:2021
- 资助金额:
$ 61.6万 - 项目类别:
Predicting context-specific molecular and phenotypic effects of genetic variation through the lens of the cis-regulatory code
通过顺式调控密码的视角预测遗传变异的特定背景分子和表型效应
- 批准号:
10659170 - 财政年份:2021
- 资助金额:
$ 61.6万 - 项目类别:
Predicting context-specific molecular and phenotypic effects of genetic variation through the lens of the cis-regulatory code
通过顺式调控密码的视角预测遗传变异的特定背景分子和表型效应
- 批准号:
10297562 - 财政年份:2021
- 资助金额:
$ 61.6万 - 项目类别:
Predicting context-specific molecular and phenotypic effects of genetic variation through the lens of the cis-regulatory code
通过顺式调控密码的视角预测遗传变异的特定背景分子和表型效应
- 批准号:
10474459 - 财政年份:2021
- 资助金额:
$ 61.6万 - 项目类别:
Multi-omic functional assessment of novel AD variants using high-throughput and single-cell technologies
使用高通量和单细胞技术对新型 AD 变体进行多组学功能评估
- 批准号:
10684210 - 财政年份:2021
- 资助金额:
$ 61.6万 - 项目类别:
Multi-omic functional assessment of novel AD variants using high-throughput and single-cell technologies
使用高通量和单细胞技术对新型 AD 变体进行多组学功能评估
- 批准号:
10217784 - 财政年份:2021
- 资助金额:
$ 61.6万 - 项目类别:
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